1. Field
Embodiments of the present invention generally relate to task software, and, more particularly, to a method and apparatus for recommending work artifacts based on collaboration events.
2. Description of the Related Art
In collaborative environments, diverse groups of people contribute to an expanding pool of resources in a largely unstructured and un-curated manner. The resulting information overload makes it difficult for a user to locate relevant items and stay informed of relevant changes made by others. Standard approaches to finding relevant items and changes, such as search, browsing, rule-based filters and recent items lists, have limitations in collaborative environments. In collaboration systems (such as wikis, task managers, and Sharepoint) there is a critical need to find task-relevant artifacts (e.g., documents, webpages, and project files). This information retrieval process is more challenging in collaborative than non-collaborative environments for several reasons: (1) a large number of contributors leads to an overwhelming number of resources, (2) many resources are made available to all users but only interest a small fraction of users, and (3) the content is difficult to curate, resulting in out-of-date items and many duplicated-then-slightly-changed items, which obscures the true items of interest.
Successful browsing requires familiarity with the navigational hierarchy of the item or topic, and even this presupposes that the navigation hierarchy was designed appropriately. Browsing quickly breaks down in large repositories, the likes of which are often characteristic of an active collaborative environment with multiple contributors. Rules-based filtering requires users to describe and anticipate ahead of time the type of items that would potentially be important. User studies with Task Assistant, disclosed in U.S. patent application Ser. No. 12/476,020, which is hereby incorporated by reference in its entirety, have shown that users are poor at judging ahead of time the kinds of information that they would define as important. Filters are potentially successful when they properly define the characteristics of known items of interest. However, defining unknown items of interest, which are common to collaborative environments, is difficult, if not impossible. Although search is also a powerful means of information retrieval, it requires the user to know something concrete and differentiating about the target item in order to formulate a successful query. The same holds true for items the user is unaware of, which occurs frequently in fast paced collaborative environments.
Most current work in “recommender systems” (i.e., systems that identify information that may be important to a user) focuses on large datasets, where users explicitly rate or otherwise express an interest in items. Examples include movie-review and online shopping sites, which may have thousands of users and millions of ratings or purchase events. These recommender systems focus on intrinsic properties of the items being recommended (e.g., features of movies or products), and the characteristics of the users choices in the system. The techniques used for these systems do not work well for smaller domains, such as for documents that are very specific to an organization, nor do they work for items just recently created, which have few properties beyond a name associated with them
Therefore, there is a need in the art for a method and apparatus for recommending more relevant work artifacts in a collaborative environment.